{"id":"W2991503924","doi":"10.1021/acssynbio.9b00222","title":"Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from <i>in Vitro</i> Evolution","year":2019,"lang":"en","type":"article","venue":"ACS Synthetic Biology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Foundation for Innovation; Government of Ontario","keywords":"Aptamer; Computational biology; Molecular evolution; Systematic evolution of ligands by exponential enrichment; Folding (DSP implementation); Sequence (biology); DNA; Biology; Directed Molecular Evolution; Directed evolution; Genetics; Phylogenetic tree; Gene; RNA; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258217,0.0001694456,0.0003281765,0.0001059382,0.0000307222,0.000002671246,0.000157785,0.0003027534,0.00003222884],"category_scores_gemma":[0.0000765422,0.0001428022,0.0001442131,0.0001452047,0.000192413,0.000004635998,0.000103081,0.00009756301,0.00001944093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003812361,"about_ca_system_score_gemma":0.00004379635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000219715,"about_ca_topic_score_gemma":0.00001600628,"domain_scores_codex":[0.9988289,0.0001288431,0.0003078752,0.0004303533,0.00008398829,0.0002200004],"domain_scores_gemma":[0.9992954,0.00005509187,0.0001619961,0.0003674612,0.00008630069,0.00003374036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003423469,0.0000766922,0.03669024,0.000003372538,0.0001104284,6.037246e-7,0.000006268448,0.00000823899,0.9607475,0.0001553834,0.00007988719,0.001779019],"study_design_scores_gemma":[0.001798553,0.0006220764,0.0476669,0.00003678132,0.0001312527,0.00001428446,0.0002024319,0.0005541755,0.9319992,0.003398442,0.01296938,0.0006064897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959775,0.0005055028,0.002265132,0.0001891915,0.0001905466,0.0001653106,0.0001356031,0.00003098188,0.0005402361],"genre_scores_gemma":[0.9965758,0.0001314415,0.002353217,0.0001565052,0.00003255443,0.000007066798,0.0004315308,0.00001269602,0.0002992406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02874829,"threshold_uncertainty_score":0.5823305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006438396397151666,"score_gpt":0.2362827670719613,"score_spread":0.2298443706748096,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}